Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems最新文献

{"title":"Computing with Near Data","authors":"Xulong Tang, M. Kandemir, H Zhao, Myoungsoo Jung, Mustafa Karaköy","doi":"10.1145/3309697.3331487","DOIUrl":"https://doi.org/10.1145/3309697.3331487","url":null,"abstract":"The cost of moving data between compute elements and storage elements plays a significant role in shaping the overall performance of applications. We present a compiler-driven approach to reducing data movement costs. Our approach, referred to as Computing with Near Data (CND), is built upon a concept called \"recomputation\", in which a costly data access is replaced by a few less costly data accesses plus some extra computation, if the cumulative cost of the latter is less than that of the costly data access. Experimental result reveals that i) the average recomputability across our benchmarks is 51.1%, ii) our compiler-driven strategy is able to exploit 79.3% of the recomputation opportunities presented by our workloads, and iii) our enhancements increase the value of the recomputability metric significantly.","PeriodicalId":268462,"journal":{"name":"Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","volume":"313 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114294663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Competitive Online Optimization under Inventory Constraints","authors":"Qiulin Lin, Hanling Yi, John Z. F. Pang, Minghua Chen, A. Wierman, Michael Honig, Yuanzhang Xiao","doi":"10.1145/3309697.3331495","DOIUrl":"https://doi.org/10.1145/3309697.3331495","url":null,"abstract":"This paper studies online optimization under inventory (budget) constraints. While online optimization is a well-studied topic, versions with inventory constraints have proven difficult. We consider a formulation of inventory-constrained optimization that is a generalization of the classic one-way trading problem and has a wide range of applications. We present a new algorithmic framework, CR-Pursuit, and prove that it achieves the optimal competitive ratio among all deterministic algorithms (up to a problem-dependent constant factor) for inventory-constrained online optimization. Our algorithm and its analysis not only simplify and unify the state-of-the-art results for the standard one-way trading problem, but they also establish novel bounds for generalizations including concave revenue functions. For example, for one-way trading with price elasticity, CR-Pursuit achieves a competitive ratio within a small additive constant (i.e., 1/3) to the lower bound of lnӨ+1, where Ө is the ratio between the maximum and minimum base prices.","PeriodicalId":268462,"journal":{"name":"Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122422158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online Optimization in Cloud Resource Provisioning: Predictions, Regrets, and Algorithms","authors":"Joshua Comden, Sijie Yao, Niangjun Chen, H. Xing, Zhenhua Liu","doi":"10.1145/3309697.3331516","DOIUrl":"https://doi.org/10.1145/3309697.3331516","url":null,"abstract":"Several different control methods are used in practice or have been proposed to cost-effectively provision IT resources. Due to the dependency of many control methods on having accurate predictions of the future to make good provisioning decisions, there has been a great deal of literature on prediction workload demand. However, even with all of this literature on workload predictions and their utilization in control algorithms, the understanding of prediction error and how to handle it remains an important open issue and research challenge. In this paper we aim to mend this gap by making the following contributions: (i) Prediction error is modeled to aid in proving worst-case dynamic regret bounds for control algorithms. (ii) Upper bounds on dynamic regret are proven for a variety of algorithms in terms of the prediction error model. In order to choose which algorithm to run without prediction error knowledge, a simple online meta-algorithm is designed. (iii) A detailed analysis of prediction accuracy is done for cloud computing by fitting real-world CPU utilization traces of Azure virtual machines to popular prediction models. (iv) Using real-world trace based simulations of CPU allocation for virtual machines, the proposed meta-algorithm is shown to outperform a popular algorithm selection policy and perform very closely to that of the best algorithm chosen in hindsight.","PeriodicalId":268462,"journal":{"name":"Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134190281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance Analysis of Workload Dependent Load Balancing Policies","authors":"T. Hellemans, Tejas Bodas, B. Van Houdt","doi":"10.1145/3309697.3331504","DOIUrl":"https://doi.org/10.1145/3309697.3331504","url":null,"abstract":"Load balancing plays a crucial role in achieving low latency in large distributed systems. Recent load balancing strategies often rely on replication or use placeholders to further improve latency. However assessing the performance and stability of these strategies is challenging and is therefore often simulation based. In this paper we introduce a unified approach to analyze the performance and stability of a broad class of workload dependent load balancing strategies. This class includes many replication policies, such as replicate below threshold, delayed replication and replicate only small jobs, as well as strategies for fork-join systems. We consider systems with general job size distributions where jobs may experience server slowdown. We show that the equilibrium workload distribution of the cavity process satisfies a functional differential equation and conjecture that the cavity process captures the limiting behavior of the system as its size tends to infinity. We study this functional differential equation in more detail for a variety of load balancing policies and propose a numerical method to solve it. The numerical method relies on a fixed point iteration or a simple Euler iteration depending on the type of functional differential equation involved. We further show that additional simplifications can be made if certain distributions are assumed to be phase-type. Various numerical examples are included that validate the numerical method and illustrate its strength and flexibility.","PeriodicalId":268462,"journal":{"name":"Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132563072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inferring Catchment in Internet Routing","authors":"Pavlos Sermpezis, Vasileios Kotronis","doi":"10.1145/3309697.3331493","DOIUrl":"https://doi.org/10.1145/3309697.3331493","url":null,"abstract":"BGP is the de-facto Internet routing protocol for interconnecting Autonomous Systems (AS). Each AS selects its preferred routes based on its routing policies, which are typically not disclosed. Due to the distributed route selection and information hiding, answering questions such as \"what is the expected catchment of the anycast sites of a content provider at the AS-level, if new sites are deployed?\", or \"how will load-balancing behave if an ISP changes its routing policy for a prefix?\", is a hard challenge. In this work, we propose a framework and methodology to infer the routing behavior in existing or hypothetical routing configurations, and provide new capabilities and insights for informative route inference (e.g., isolating the effect of randomness that is present in prior simulation-based approaches). The proposed framework can be useful in a number of applications: measurements/monitoring, traffic engineering, network planning, Internet routing models, etc.","PeriodicalId":268462,"journal":{"name":"Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114521108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Demystifying Complex Workload-DRAM Interactions: An Experimental Study","authors":"Saugata Ghose, Tianshi Li, Nastaran Hajinazar, Damla Senol Cali, O. Mutlu","doi":"10.1145/3309697.3331482","DOIUrl":"https://doi.org/10.1145/3309697.3331482","url":null,"abstract":"It has become increasingly difficult to understand the complex interaction between modern applications and main memory, composed of Dynamic Random Access Memory (DRAM) chips. Manufacturers and researchers are developing many different types of DRAM, with each DRAM type catering to different needs (e.g., high throughput, low power, high memory density). At the same time, the memory access patterns of prevalent and emerging applications are rapidly diverging, as these applications manipulate larger data sets in very different ways. As a result, the combined DRAM-workload behavior is often difficult to intuitively determine today, which can hinder memory optimizations in both hardware and software. In this work, we identify important families of workloads, as well as prevalent types of DRAM chips, and rigorously analyze the combined DRAM-workload behavior. To this end, we perform a comprehensive experimental study of the interaction between nine different DRAM types (DDR3/4, LPDDR3/4, GDDR5, Wide I/O, Wide I/O 2, HBM, HMC) and 115 modern applications and multiprogrammed workloads from six diverse application families (desktop/scientific, server/cloud, multimedia acceleration, network acceleration, GPGPU, OS routines). We draw 12 key observations from our characterization, enabled in part by our development of new metrics that quantify the effect of memory access patterns on hardware utilization. We highlight our five most significant observations here: (1) Despite having 50% higher memory bandwidth than DDR3, the newer DDR4 rarely outperforms DDR3 on the applications we evaluate, as DDR4's access latency is 11-14% higher. (2) The high-bandwidth HMC does not outperform DDR3 for most single-thread workloads and many multithreaded applications. This is because HMC's design trade-offs (e.g., a row width that is 97% smaller than DDR3) fundamentally limit opportunities for exploiting spatial locality. For example, single-thread desktop and scientific applications actually perform 5.8% worse with HMC than with DDR3, on average, even though HMC offers 87.4% more memory bandwidth. HMC provides significant performance improvements over other DRAM types in cases where application spatial locality is low(or is destroyed), such as highly-memory-intensive multiprogrammed workloads. (3) While low-power DRAM types typically perform worse than standard-power DRAM for most memory-intensive applications, some low-power DRAM types perform well when bandwidth demand is very high. For example, on average, LPDDR4 performs only 7.0% worse than DDR3 for our multiprogrammed desktop workloads, while consuming 68.2% less energy, and Wide I/O 2 performs 2.3% better than DDR3 for multimedia acceleration. (4) The best DRAM for a heterogeneous system depends heavily on the predominant function(s) performed by the system. We study three types of applications for heterogeneous systems. First, multimedia acceleration benefits most from high-throughput memories that exploit a high amo","PeriodicalId":268462,"journal":{"name":"Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115030782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Pricing of Relocating Resources in Large Networks: Extended Abstract","authors":"S. Balseiro, David B. Brown, Chen Chen","doi":"10.1145/3309697.3331512","DOIUrl":"https://doi.org/10.1145/3309697.3331512","url":null,"abstract":"We study dynamic pricing of resources that are distributed over a network of locations (e.g., shared vehicle systems and logistics networks). Customers with private willingness-to-pay sequentially request to relocate a resource from one location to another. We focus on networks with a hub-and-spoke structure. We develop a dynamic pricing policy and a performance bound based on a Lagrangian relaxation. This relaxation decomposes the problem over spokes and is thus far easier to solve than the original problem. We analyze the performance of the Lagrangian-based policy and focus on a large network regime in which the number of spokes (n) and number of resources grow at the same rate. We show that our policy loses no more than $OBig(sqrtłn n/n Big)$ in performance compared to an optimal policy, thus implying asymptotic optimality as n grows large. We provide examples that show that upper bounds and static policies based on fluid relaxations fail to work well in this asymptotic regime. Finally, we discuss how our approach extends to more general networks involving multiple, interconnected hubs.","PeriodicalId":268462,"journal":{"name":"Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129419587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A New Flexible Multi-flow LRU Cache Management Paradigm for Minimizing Misses","authors":"Guocong Quan, Jian Tan, A. Eryilmaz, N. Shroff","doi":"10.1145/3309697.3331509","DOIUrl":"https://doi.org/10.1145/3309697.3331509","url":null,"abstract":"The Least Recently Used (LRU) caching and its variants are used in large-scale data systems in order to provide high-speed data access for a wide class of applications. Nonetheless, a fundamental question still remains: in order to minimize the miss probabilities, how should the cache space be organized to serve multiple data flows? Commonly used strategies can be categorized into two designs: pooled LRU (PLRU) caching and separated LRU (SLRU) caching. However, neither of these designs can satisfactorily solve this problem. PLRU caching is easy to implement and self-adaptive, but does not often achieve optimal or even efficient performance because its set of feasible solutions are limited. SLRU caching can be statically configured to achieve optimal performance for stationary workload, which nevertheless could suffer in a dynamically changing environment and from a cold-start problem. To this end, we propose a new insertion based pooled LRU paradigm, named I-PLRU, where the data flows can be inserted at different positions of a pooled cache. This new design can achieve the optimal performance of the static SLRU, but retains the adaptability of PLRU for resource sharing. Theoretically, we characterize the asymptotic miss probabilities of I-PLRU, and prove that, for any given SLRU design, there always exists an I-PLRU configuration that achieves the same asymptotic miss probability, and vice versa. We next design a policy to minimize the miss probabilities. However, the miss probability minimization problem turns out to be non-convex under the I-PLRU paradigm. Notably, we utilize an equivalence mapping between I-PLRU and SLRU to efficiently find the optimal I-PLRU configuration. We prove that I-PLRU outperforms PLRU and achieves the same miss probability as the optimal SLRU under a stationary request arrival process. From an engineering perspective, the flexibility of I-PLRU avoids separating the memory space, supports dynamic and refined configurations, and alleviates the cold-start problem, potentially yielding better performance than both SLRU and PLRU.","PeriodicalId":268462,"journal":{"name":"Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128322966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Queue and Loss Distributions in Finite-Buffer Queues","authors":"F. Ciucu, Felix Poloczek, Amr Rizk","doi":"10.1145/3309697.3331496","DOIUrl":"https://doi.org/10.1145/3309697.3331496","url":null,"abstract":"We derive simple bounds on the queue distribution in finite-buffer queues with Markovian arrivals. The bounds capture a truncated exponential behavior, involving joint horizontal and vertical shifts of an exponential function; this is fundamentally different than existing results capturing horizontal shifts only. We also obtain similar bounds on the loss distribution, which is a key metric to understand the impact of finite-buffer queues on real-time applications. Simulations show that the bounds are accurate in heavy-traffic regimes, and improve existing ones by orders of magnitude.","PeriodicalId":268462,"journal":{"name":"Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122432437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Axiomatizing Congestion Control","authors":"Doron Zarchy, R. Mittal, Michael Schapira, S. Shenker","doi":"10.1145/3309697.3331501","DOIUrl":"https://doi.org/10.1145/3309697.3331501","url":null,"abstract":"Recent years have witnessed a revival of both industrial and academic interest in improving congestion control designs. The quest for better congestion control is complicated by the extreme diversity and range of (i) the design space (as exemplified by the stark conceptual and operational differences between recent proposals~citebbr,vivace, COPA ), (ii) the desired properties (ranging from high performance to fairness to TCP-friendliness), (iii) the envisioned operational setting (inter- and intra-datacenter, wireless, the commercial Internet, satellite), and (iv) the application loads and requirements (small vs. large traffic demands, latency- vs. bandwidth-sensitive). Most congestion control research uses simulation and experiments under a limited range of network conditions. This is extremely important for understanding the detailed performance of particular schemes in specific settings, but provides limited insight into the more general properties of these schemes and no information about the inherent limits %of congestion control designs (such as, which properties are simultaneously achievable and which are mutually exclusive). In contrast, traditional theoretical approaches are typically focused on the design of protocols that achieve specific, predetermined objectives (e.g., network utility maximization~citekelly2009 ), or the analysis of specific protocols (e.g., from control-theoretic perspectives~citepaganini2003control ), as opposed to exploring the inherent tensions/derivations between desired properties. We advocate an axiomatic approach to congestion control, which is complementary to the experimental and theoretical work currently being pursued. Our approach, modeled on similar efforts in social choice theory and game theory~citearrow1950difficulty, identifies a set of requirements (\"axioms'') and then identifies (i) which of its subsets of requirements can coexist (i.e., there are designs that achieve all of them) and which subsets cannot be met simultaneously (i.e., no design can simultaneously achieve all of them), and (ii) whether some requirements immediately follow from satisfying other requirements. Thus, the axiomatic approach can shed light on the inherent tradeoffs involved in congestion control protocol design, and can be leveraged to classify existing and proposed solutions according to the properties they satisfy. The axiomatic approach has been applied to many computer science environments, e.g. reputation systems, recommendation systems, link prediction, and more. To the best of our knowledge, ours is the first application of this approach to congestion control protocols (though citeshenker1990theoretical touches on the subject briefly). We introduce a simple network model where we can evaluate congestion control designs and formulate several natural axioms (or requirements) for congestion control protocols, including efficient link-utilization, loss-avoidance, fairness, stability, and TCP-friendliness. Congestion co","PeriodicalId":268462,"journal":{"name":"Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123300495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}